Credit Derivatives, Corporate News, and Credit RatingsCredit Derivatives, Corporate News, and Credit...

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Credit Derivatives, Corporate News, and Credit Ratings

Lars Norden ∗

Department of Banking and Finance, University of Mannheim,

L 5, 2, D-68131 Mannheim, Germany

First version: May 28, 2008; this version: October 18, 2008

Abstract

The market for credit default swaps (CDS) represents an interesting venue to study if and how public and private information is incorporated in market prices. This OTC market is neither regulated nor supervised and exclusively made up by institutional traders that buy and sell credit risk. Considering the key importance of rating announcements for market participants, this paper investigates announcement and anticipation effects, conditioning on public information and using proxies for private information about CDS underlyings. Analyzing an international sample of frequently traded firms during the period 2000-2005 on a daily basis yields the following results. First, CDS markets significantly react to rating downgrades and, even stronger, to reviews for downgrade, while the magnitude of anticipation effects is the other way round. Second, the CDS market response is stronger for firms with high general media coverage. Moreover, rating-related wire news prior to rating events significantly explain the anticipation of CDS markets when general media coverage is high. Third, the run up is more pronounced the higher a firm’s number of major bank lenders and there is a signifcantly higher number of days with large positive abnormal price changes and no news (or no negative news) before rating events than in the full sample. These findings are consistent with the view that private information of banks spills over to these markets through their CDS trading.

Keywords: Informational efficiency; Credit default swaps; Media coverage; Insider trading

JEL classification: G14; G20; D8

∗ Tel.: +49 621 1811536; fax: +49 621 1811534. E-mail address: [email protected] (L. Norden). I am grateful to Utpal Bhattacharya, André Güttler, Gunter Löffler, Philipp Schmitz, Dragon Tang, Monika Trapp, Marliese Uhrig-Homburg, Martin Weber, as well as participants at the International Conference on Price, Liquidity, and Credit Risks 2008 in Konstanz and the Workshop in Banking and Finance at the University of Mannheim for helpful comments and suggestions. Thomas Gelb, Andreas Waschto and Kathrin Schlafmann provided excellent research assistance. The first draft of this paper was completed when I was visiting the Finance Department, Kelley School of Business, Indiana University. Financial support from the German Research Foundation (Deutsche Forschungsgemeinschaft) is gratefully acknowledged.

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1. Introduction

In theory financial markets are assumed to be efficient in the sense that prices immediately

reflect publicly available information. While there is comprehensive evidence on this

hypothesis for various markets, little is known about the informational efficiency of the

heavily growing market for credit derivatives.

Credit derivatives, in particular single name credit default swaps (CDS), represent an

interesting opportunity to study the interplay of public and private information and its effect

on market prices for several reasons. First, unlike in the stock markets there are exclusively

institutional traders (e.g., commercial and investment banks, insurance companies, and hedge

funds) whose trading behavior is very focused and predominantly influenced by information

that relates to credit risk. The latter is available to the public through, e.g., corporate financial

statements, credit ratings, and a continuous stream of news in the media. Second, in addition

to public information about firms that are traded in CDS markets there may also be a

significant amount of private information because large banks are frequently lender or

underwriter and CDS trader at the same time, having access to private information about their

borrowers through their screening and monitoring activities (Acharya and Johnson, 2007a).

Therefore, private information may affect banks’ activities in CDS markets (e.g., British

Banker’s Association, 2006; Minton, Williamson, and Stulz, 2008) and can be valuable in

various situations and irrespective of banks’ trading motives. Third, trading in credit

derivatives is non-anonymous from the perspective of market participants but relatively

opaque from an outside perspective since this OTC market is not subject to any regulation or

supervision and financial reporting follows minimum standards for off-balance sheet items.

This paper investigates if and how the CDS market responds to credit rating

announcements conditional on public and private information prior to these events. The first

contribution of this paper is that public information is directly measured by means of the

general media coverage of CDS underlyings as well as the intensity and content of daily

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corporate news. This approach circumvents the various problems inherent to studies that

consider prices from other markets (e.g., the stock market) as an indirect benchmark for

public information. As mentioned by Jorion and Zhang (2007) this issue becomes critical

when studying information events that relate to credit risk because the direction and

magnitude of market responses might differ depending on the type of security and efficiency

of the markets (stock, bond, option, future, etc.). The second contribution of this paper is that

the influence of private information about the traded firms is also examined. Private

information is proxied by the firms’ number of major bank lenders (lead arrangers) in the

market for large commercial and syndicated loans because these banks have preferential

access to private information and are the most important participants in CDS markets at the

same time. On top of that, I examine uncontaminated 20-trading day intervals before rating

announcements to detect days with significantly abnormal CDS spread changes and no public

information. A significantly higher fraction of theses days in windows before rating events

than in the full sample would be consistent with a clustering of private information-based

trading.

Analyzing an international sample of 95 firms that are frequently traded in CDS markets

during the period 2000-2005 (including 148,580 firm-day observations) yields the following

key findings. First, CDS markets significantly react to rating downgrade announcements and,

even stronger, to reviews for downgrade which confirms results from related studies with data

from a longer and more recent period (e.g., Hull, Predescu, and White, 2004; Norden and

Weber, 2004; Micu, Remolona, and Wooldridge, 2006). The magnitude of anticipation effects

(run up) is weaker for reviews for downgrade than for actual downgrades. Second, public

information measured by the general media coverage of the traded firms significantly affects

the strength of announcement and anticipation effects. Moreover, rating-related wire news

prior to rating events significantly explain the run up of CDS markets when the general media

coverage of the firms is high. Finally, I provide evidence that suggests that private

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information also drives CDS markets before rating announcements. The market’s anticipation

is more pronounced the higher the number of major bank lenders of the traded firms,

consistent with the view that private information of these lenders spills over to markets

through their CDS trading. In addition, there is a significant clustering of days with no news

(or no negative news) during the month before negative rating events that exhibit significantly

positive abnormal CDS spread changes. Interestingly, the latter result cannot be found in the

case of positive rating announcements, suggesting that insider trading in CDS markets is

asymmetric, i.e. more likely to occur before credit quality deteriorations than before

improvements.

The remainder of this paper is organized as follows. Section 2 reviews the related

literature. Section 3 describes the data set. Section 4 explains the methodology and reports

findings from the baseline analysis. Section 5 examines the influence of public information on

the CDS market response to rating announcements and Section 6 the influence of private

information. Section 7 summarizes results from further empirical checks that examine the

robustness of previous findings. Section 8 concludes.

2. Related literature

This study relates to the literatures on informational efficiency of financial markets (starting

with Fama, 1970, and which has been reviewed extensively in other papers), information

production by rating agencies (e.g., Löffler, 2005; Boot, Milbourn, and Schmeits, 2006;

Güttler and Wahrenburg, 2007; Hirsch and Krahnen, 2007), and to the relatively new field of

research on credit derivatives. Since the focus of this paper is on CDS markets, I subsequently

summarize key insights from the existing literature on (i) announcement and anticipation

effects of rating events, (ii) insider trading, and (iii) on the co-movement with other markets

(e.g., stock, bond, options, and loan markets).

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First, there is evidence that CDS markets display a significant anticipation of rating

announcements (e.g., Hull, Predescu, and White, 2004; Norden and Weber, 2004; Micu,

Remolona, Wooldridge, 2006). These studies show that CDS markets exhibit a significant

reaction to rating downgrades and an even stronger response to announcements of reviews for

downgrades. In addition, a significant anticipation of rating events by CDS markets has been

detected. However, except the finding that the reaction is less pronounced if downgrades have

been preceded by reviews for downgrade these studies do not explain why there is anticipation

of rating events in CDS markets. In particular, there is no evidence on if and how public and

private information affect CDS markets prior to rating announcements.

Second, CDS spread changes appear to have significant predictive power for future stock

returns, in particular prior to adverse changes of the credit quality of the traded firms

(Acharya and Johnson, 2007a). Interestingly, the effect becomes stronger the higher the

number of bank relationships of the CDS underlyings. This result is consistent with the view

that there is insider trading in CDS markets. In contrast, this paper measures public

information directly instead of indirectly considering the stock market as a benchmark for

public information. This is a key advantage when analyzing information events like rating

downgrades because the latter can be associated with positive or negative stock market

reactions depending on the reason for the rating change (increase of leverage vs. decrease of

profitability; Goh and Ederington, 1993) while the prediction for the impact of negative

rating-related news on CDS spreads is unambiguous (e.g., Jorion and Zhang, 2007).

Furthermore, there is also some indication for insider trading before private equity buyouts

(Acharya and Johnson, 2007b). The magnitude of suspicious activities in CDS markets is

stronger the more bank lenders (= potential traders in CDS markets) in the financing

syndicate.

Third, the CDS market tends to lead the bond market and contributes more to price

discovery than the latter (e.g., Blanco, Brennan, and Marsh, 2005; Houweling and Vorst,

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2005; Zhu, 2006; Norden and Weber, 2007). In addition, these studies find that lagged stock

returns significantly explain contemporaneous CDS spread changes in firm-specific time-

series analysis. Berndt and Ostrovnaya (2008) investigate the link between CDS, equity and

equity options markets. They detect a clear spillover from CDS to equity markets around

adverse earnings releases. In addition, there is evidence that the equity market does not

respond to abnormal changes in options prices prior to negative credit news unless the

information has also been reflected by CDS spreads. Similarly, Callen, Livnat, and Segal

(2007) provide evidence that CDS spread changes are inversely correlated with quarterly

earning announcements and earnings surprises, i.e. higher profits reduce the risk of default in

the short-run. Interestingly, they also show that CDS spread changes are positively

(negatively) associated with the accruals (cash flows) component of earnings. Furthermore,

Jorion and Zhang (2007) test for intra-industry price effects in stock and CDS markets around

different types of credit events. The analysis yields that Chapter 11 bankruptcies create

contagion effects, i.e. credit spreads of non-defaulting firms from the same industry also

increase, and Chapter 7 bankruptcies create competitive effects, i.e. credit spreads of

“surviving” firms from the same industry decrease. It is noteworthy that these effects can be

better observed in CDS markets than in stock markets. Finally, there is evidence that CDS

markets also affect traditional loan markets. For example, Hirtle (2007) shows that a greater

use of credit derivatives increases banks’ credit supply to large firms and that loan maturities

becomes longer and loan spreads lower. Ashcraft and Santos (2007) document adverse effects

for the funding costs of risky and opaque firms and a small positive effect for low risk and

transparent firms. Futhermore, Norden and Wagner (2007) find that the pricing of bank loans

to large firms is significantly positively related to price information from CDS markets and

that this link has become more immediate and more pronounced in recent years.

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3. Data

The data set consists of an international sample of 95 firms that have frequently been traded in

the CDS market (see Appendix A). The sample includes 148,580 firm-day observations,

spanning the period from January 2000 to December 2005. The average number of

observations per firm amounts to 1,564, allowing for robust time-series econometrics. The

data covers the U.S. (33%) and Europe (67%) as well as industrial firms (68%) and banks

(32%). Note that the sample period includes up- and downswings in CDS markets as well as

years with predominantly positive or negative rating announcements, i.e. the results are not

biased towards particular market movements.

The data set has been assembled from the following main sources. First, firm-specific time

series of daily closing CDS spreads are provided by CreditTrade and one large European

bank. I focus on single name CDS spreads that refer to contracts of five year maturity and

senior unsecured debt since these have emerged as the benchmark in CDS trading. Firms are

included in the sample if there were at least 100 CDS spreads in each of the years 2000-2005.

Gaps in CDS spread time series have been filled by interpolating between days with available

CDS spreads, following related studies (e.g., Blanco, Brennan, and Marsh, 2005; Zhu, 2006).

Second, I have collected extensive data on credit ratings and rating announcements from

Bloomberg. The data covers the three major rating agencies (Standard & Poor’s, Moody’s,

and Fitch Ratings) and two different types of events (actual rating changes and reviews/watch

listings). I focus on rating changes and rating reviews since these events are decided by the

rating committee (e.g., Boot, Milbourn, and Schmeits, 2006; Hirsch and Krahnen, 2007)

whereas so-called “rating outlooks” (not considered here) are under the discretion of

individual rating analysts. Accordingly, the analysis can be differentiated by agency, direction

of the rating change (up or down), and type of the announcement (rating change or rating

review).

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Third and most important, to measure the amount of public information the previous data

has been merged with news stories from FACTIVA, the world’s largest available database on

corporate news. In the analysis, I measure public information by means of the general media

coverage of the firms traded in CDS markets over the period from 2000 to 2006 and the

intensity and content of daily corporate news (e.g., number of news per day, number of words

per day, and content in headlines) prior to rating announcements. The first measure, general

media coverage in English language, is proxied by the following four variables: (i) the total of

all news stories (MEDIA1), (ii) the total of news wire stories (MEDIA2), (iii) the total of all

news stories matching the search items “rating” or “downgrade” or “upgrade” in the full text

(MEDIA3), and (iv) the total of news wire stories matching the search items “rating” or

“downgrade” or “upgrade” in the full text (MEDIA4). Note that the previous four variables

are firm-specific but time-invariant.

Therefore, I also consider rating-related daily corporate news as a second measure of

public information which is firm-specific and time-varying. Accordingly, I have downloaded

the full text of all news wire stories1 matching the search items “rating” or “downgrade” or

“upgrade” in the full text for every firm, resulting in a total of 240,200 text files. I focus on

wire news (“ticker news” like Dow Jones News Service, Reuters, AFX, and electronic press

releases, etc.) because this is the key source of information for traders in CDS markets. It is

most likely that stories on tickers and news wires precede or coincide with newspaper articles

and are, therefore, of key importance for institutions participating in CDS markets. For each

news story I observe the date and time, the headline, the full text, the names of companies,

industries, and news subjects that relate to the story (assigned by FACTIVA) and the total of

words. Note that I have assigned news that were released on Saturdays or Sundays (1.97% of

1News stories from the media have been analyzed in other studies to investigate market sentiment, attention of individual investors and the role of soft information in stock markets (e.g. Tetlock 2007; Schmitz, 2007; Gaa, 2007; Engelberg, 2008). However, most of these studies consider articles or columns from newspapers and investor magazines because the focus is on individual investors.

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the total) to the next trading day. Eventually, as by-product I used the news stories from

FACTIVA to double-check the dates of the rating announcements collected from Bloomberg.

Finally, I proxy for private information in the CDS market by considering the number of

major bank relationships of the firms traded as CDS underlyings (Acharya and Johnson,

2007a). The number of bank relationships (measured at the parent company level for firms

and banks) has been manually collected from LPC Deal Scan and refers to the number of lead

banks for large commercial and syndicated loans. Table 1 summarizes the data.

(Insert Table 1 here)

During the period 2000-2005 the cross-sectional time series mean CDS spread amounts to

62 basis points (median = 37 basis points), varying from 4 to 1250 basis points. The mean

percentage bid-ask spread of CDS is 22%. In addition, most of the firms are rated A (S&P:

50%, Moody’s: 43%, Fitch: 39%). The sample includes a total of 766 rating announcements

(hereof: 269 reviews for downgrade and 339 actual downgrades). Moreover, the mean general

media coverage (MEDIA1) is 47,667 news per firm during the period 2000-2006, with a

minimum of 5,629 (Metro AG) and 223,686 (Ford Motor Company).2 The measures

MEDIA1 (MEDIA2) and MEDIA3 (MEDIA4), i.e. different sources of information (all news

vs. wire news), exhibit a Spearman rank correlation coefficient of 0.62 (0.70), while MEDIA1

(MEDIA3) and MEDIA2 (MEDIA4), i.e. different types of information (all vs. rating-

related), exhibit rank correlations of 0.74 (0.78). Considering daily firm-specific rating-related

wire news, the mean (median) number of news per day is 1.62 (0.00) and the maximum is

220. The mean (median) number of words per story and day is 665 (538). Positive news are

2 General media coverage is positively (but not perfectly) correlated with firm size. For example, the rank correlation between MEDIA1 and the average market capitalization of the firms (in Euro) amounts to 0.59.

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slightly more often released than negative news3 while news including “upgrade” and

“downgrade” are relatively similar and rather rare. Finally, the number of major bank lenders

collected from LPC Deal Scan ranges between 1 and 16 with a median of 6.4

4. The CDS market response to rating events: Methodology and baseline results

The main purpose of this section is to connect to the existing literature by documenting the

CDS market response to rating announcements on a more comprehensive dataset than

previous studies before explaining the CDS market response relates to measures of public and

private information. Consequently, this part of the analysis represents an out-of-sample test of

earlier studies based on a longer time series including more recent data (e.g., Hull, Predescu,

and White, 2004; Norden and Weber, 2004; Micu, Remolona, and Wooldridge, 2006). First, I

analyze the CDS market reaction in a univariate event study by type of the rating

announcement and by rating agencies separately and, second, I examine the announcement

effects in a multivariate setting across and within event types and agencies. In addition, I will

provide evidence on anticipation effects by computing the CDS market run up for different

points in time prior to rating announcements and all event-agency combinations.

More specifically, I calculate daily first differences of each firm’s CDS spreads and

subtract changes of a rating-specific CDS index to obtain abnormal CDS spread changes

(ASCs). The CDS index corresponds to an equally-weighted index based on five-year senior

unsecured mid CDS spreads of the full universe of “CreditTrade’s Benchmark” firms. Note

3 News stories are classified as negative (positive) if they include one or more of the following content proxies: positive, good, up, strong, well, better, upgrade, optimistic, improve, increase, and raise (negative, bad, down, weak, badly, worse, downgrade, pessimistic, deteriorate, decrease, and lower). Admittedly, this definition fails to include all negative news. However, using these imperfect proxies creates a bias against finding effects since only a subset of all public information is considered. 4 For nine out of 95 firms I could not identify any lead arranger in LPC Deal Scan. Since these firms are likely to have a positive number of bank relationships (instead of no bank relationships at all) that has simply not been reported to LPC, I impute the median number of bank lenders from the full sample to these firms. Note that all subsequent results remain unchanged if I drop these firms from the sample. In addition, the number is smaller than in Acharya and Johnson (2007a) because my sample includes more European firms (which tend to have a smaller number of lead banks) than US firms and I calculate the number at the parent company level for borrowers and lenders.

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that the index is rating-specific (AAA, AA, …, BB), i.e. daily CDS spread changes of firms

that exhibit a rating “AA” on a particular day are adjusted by changes of the AA-CDS index

to control for the fact the CDS spread changes are on average larger the worse the firm’s

credit rating. Note that the results are not changed (except the magnitude of the coefficient of

the index) if I alternatively use a constant-rating CDS index (either for AA, A, or BBB ratings

which are the most prevalent in the data set5). In a next step, I transform calendar time around

rating announcements by the three major rating agencies into event time, starting 90 trading

days before and ending 20 days after the event day. Eventually, as explained above I calculate

mean abnormal CDS spread changes (ASCs) as well as mean cumulative abnormal CDS

spread changes (CASCs) for different intervals or days during the event time window [-90,

20].6 Figure 1 displays the results from the univariate event study for negative rating

announcements.7

(Insert Figure 1 here)

Figure 1a reveals that there are announcement effects at event time = 0 and that there is an

increasing run up in the CDS market before reviews for rating downgrade by all three rating

agencies. The market response reaches its maximum shortly after the announcement day,

ranging between 45 and 55 basis points. The detected pattern is qualitatively very similar to

5 Meanwhile, CDS indices based on frequently traded CDS underlyings have been created (e.g., iTraxx Europe, CDX for North America). Unfortunately, most of these indices start in the years 2004 or 2005, i.e. they cannot be used as a benchmark for the sample period from 2000 to 2005. 6 The results are qualitatively very similar for abnormal percentage changes of CDS spreads and the product of the corresponding growth factors (geometric sum of abnormal spread changes) instead of taking the arithmetic sum of first differences. In addition, I am aware of the fact that CDS spreads exhibit a decreasing time-to-maturity between standard maturity dates. This phenomenon represents no problem here since it affects the individual CDS spreads as well as the CDS index in the same way, i.e. the calculation of abnormal spread changes is consistent with respect to the underlying maturity. 7 Preliminary analyses have shown that CDS markets also exhibit a weak anticipation prior to positive rating announcements (reviews for upgrade and actual upgrades). Consistent with most of the literature on the stock and bond market response to rating events (e.g., Dichev and Piotroski, 2001; Kim and Nabar, 2007), these effects are neither statistically nor economically significant. One exception is the study of Micu, Remolona, and Wooldrigde (2006) that also documents significant effects in CDS markets before and around positive rating announcements.

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related studies, confirming their results for a longer and more recent period. Furthermore,

Figure 1b indicates that anticipation starts earlier in the case of actual rating downgrades and

is more evenly spread across pre-event time. In particular, the magnitude of the short-term run

up and the announcement effects are clearly smaller than in Figure 1a. Univariate tests for

ASCs and CASCs at different points in time during the [-90, 20] window (e.g., t-tests and

non-parametric Wilcoxon sign tests, not reported here8) confirm the statistical significance of

the results shown in Figure 1.

In a next step, I estimate a regression model to analyze the CDS market reaction on the

announcement days and the short-term run up during the window [-11, -2]. At this stage I

only examine the short-term anticipation because longer pre-event windows may be

contaminated by other rating events. The dependent variable is a firm’s raw CDS spread

change on day t. Explanatory variables in regression model (1) are the changes of the CDS

index (matching the firm’s rating on the same day), dummy variables indicating days from the

[-1, 1] interval around announcements made by the three major rating agencies (SRD, MRD,

…, FD), firms from the telecommunication industry (TEL), U.S. firms (US), and year fixed-

effects. In regression model (2) I also include dummy variables that equal to one on days from

the [-11, -2] interval before rating events to study the short-term market run up.


Table 2 reveals that there are significant announcement effects in the CDS market for

reviews for downgrade made by S&P (3.77 basis points) and Moody’s (2.68 basis points) and

for downgrades by Moody’s (2.38 basis points). These coefficients can be interpreted as

average abnormal CDS spread changes in the interval [-1, 1], i.e. changes that exceed those

8 The findings from these tests are very similar to related studies and are also confirmed by the subsequent multivariate analysis.

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that can be explained by the CDS index. Moreover, the market does not react on days with

announcements made by Fitch. Estimation results from model (2) reveal that there are

positive abnormal CDS spread changes shortly before reviews for downgrade by S&P (1.29

basis points) and Moody’s (1.32 basis points). There are negative abnormal CDS spread

changes directly before downgrades by S&P and Moody’s. Consistent with model (1), I fail to

detect any significant reaction prior to negative rating announcements by Fitch Ratings.

In addition, I compute the percentage run up of CDS markets by event type and rating

agency to quantify the speed and timing of the market anticipation. Table 3 reports the results.


It can be seen that the average run up before reviews for downgrade during [-90, -11]

ranges between 45% and 61% while the same numbers range between 73% and 86% in the

case of actual rating downgrades. This can be explained by the fact the rating reviews have

increasingly gained in importance during the last years (e.g., Hirsch and Krahnen, 2007) and

the fact that some downgrades are preceded by reviews for downgrades in a more or less

timely manner.

In summary, the above findings confirm results from related studies for a longer and more

recent dataset. The question if and how the detected market response to rating announcements

is related to measures of public and private information about the firms traded in CDS

markets is analyzed in the remainder of this paper.

5. Does public information influence the CDS market response to rating events?

5.1. The cross-sectional impact of general media coverage

Subsequently, I introduce the first measure of public information, the general media coverage

of the firms to study its impact on the CDS market response around rating announcements.

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General media coverage is by definition related to the amount (and the average frequency) of

corporate news. In addition, this measure partially captures the overall attention of CDS

traders paid to firms which, in turn, may affect the likelihood of firms being traded in CDS

markets. Note that this link holds for the CDS trading of banks irrespective of their trading

motive (trading income, credit portfolio management). Nonetheless, pure income-generating

trading, in particular CDS trading in underlyings with which a bank has no lending

relationship, is likely to be more based on public information whereas portfolio management-

driven CDS trading may be more based on private information (and therefore also about the

bank’s lending strategy towards existing or future borrowers). The main point here is that

public information may affect prices in CDS markets in various situations and irrespective of

the bank’s trading motive.

To test the influence of public information on the CDS market response to rating

announcements, I first compare the cumulative abnormal CDS spread changes of firms with

low and high general media coverage (MEDIA1, based on all news in FACTIVA). Figure 2

displays the results by event type and rating agency.


The graphical analysis reveals striking differences between firms with high and low media

coverage. It turns out that the run up of CDS markets starts earlier and is substantially

stronger for firms with high media coverage (MEDIA1=1, black line) than for those with low

media coverage (MEDIA1=0, gray line). This result holds for reviews for downgrade and

actual downgrades as well as all three rating agencies9. In particular, the results are very clear

and consistent for rating actions made by S&P and Moody’s. Moreover, it can be seen that the

9 There are some deviations from this pattern in the case of rating announcements made by Fitch (Fig. 2c and 2f). This finding is not surprising given the previous results and will be revisited in the next Section.

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announcement effects (peaks in event time = 0) are relatively large for firms with low media

coverage compared to both the pre-event run up and to firms with high media coverage.

Interestingly, these results complement the evidence on the relation between CDS spread

changes and quarterly earnings announcements (e.g., Callen, Livnat, and Segal, 2007; Berndt

and Ostrovnaya, 2008).

In a next step, I investigate how CDS markets respond to rating announcements by means

of multivariate regression models and conditional on the firms’ general media coverage. For

this purpose, I reestimate the baseline regression model for subsamples of firms with low and

high media coverage. The differentiation is based on the most narrowly defined measure

(MEDIA4, media coverage based on rating-relevant wire news) but the results very similar

for the alternative measures (MEDIA1, 2, 3). Table 4 summarizes the findings.


Basically, results from the multivariate analysis are rather similar to the market reactions

that can be seen from Figure 2. While there are no significant announcement effects for firms

with low media coverage CDS markets significantly positively respond to reviews for

downgrade by S&P and Moody’s and downgrades by Moody’s for firms with high media

coverage (Panel A). In addition, the goodness of fit of the regression model explaining CDS

spread changes is considerably higher for firms with high media coverage (R2=0.0873) than

for the other firms (R2=0.0300). Furthermore, Panel B indicates that days from the pre-event

interval [-11, -2] appear to exhibit a significant influence for firms with low and high media

coverage. However, note that this analysis is not conditional on the intensity and content of

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corporate news on these days, i.e. it is not possible to conclude that individual news on these

days influence CDS spreads.10

Finally, I calculate the CDS market run up prior to rating events by event type, agency,

and general media coverage to study the speed of the market anticipation. Table 5 (similarly

to Table 3) reports the results.


The CDS market run up during the window [-90, -11] is consistently higher for firms with

high media coverage (except for downgrades announced by Fitch). In addition, the short-term

run up during [-10, -1] is higher for firms with low media coverage, indicating more

“surprising” events. Eventually, the announcements effects (event time = 0) in the CDS

market tend to be stronger for firms with low media coverage which is entirely in line with the

findings on short-term anticipation.

These results suggest that the CDS market response to rating announcements can be

explained by the overall amount of firm-specific public information. CDS markets display a

relatively large run up and small announcement effects for firms with high media coverage

and a relatively small run up and large announcement effect for firms with low media

coverage.

5.2. The influence of corporate news before rating events

In addition to the general media coverage of a firm it is not unlikely that daily corporate news

affect the way CDS markets respond to rating announcements. The general media coverage

mainly influences the average likelihood of observing news for a firm during a period of time

and may be useful proxy for the attention of CDS traders paid to individual firms. However, 10 This issue will be analyzed in the next section by taking into account the intensity and content of daily corporate news.

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since this proxy is time-invariant11 it is not possible to relate this measure directly to periods

before rating announcements. Therefore, I now examine if and how the intensity and content

of daily corporate news may influence prices in CDS markets. As mentioned beforehand, I

now focus on rating-related wire news, the subset of public information that is of key

importance for CDS traders. Most important, this measure is firm-specific and time-varying,

allowing to proxy for the amount of public information on single days. To illustrate the

intensity and content of public information before rating events, Figure 3 displays averages of

the total of rating-related wire news, the number of negative news and the number of news

including “downgrade” for the [-90, 20] window by event type.


Both Figures exhibit a large spike in all three measures on the announcement day,

indicating that the collected wire news stories from FACTIVA are indeed highly related to the

rating announcement dates retrieved from Bloomberg. Moreover, it can be seen that the

intensity of wire news (thin black line, left axis) is slightly higher before actual downgrades

than before rating reviews. This is consistent with the view that downgrades are less

surprising than rating reviews.

Given that there is rating-related public information prior to rating announcements I now

investigate more in detail when this information is released and how the information

disseminates before the events. For this purpose, I calculate the difference between the

number news stories including the words “upgrade” and “downgrade” for each firm and day,

sum up these daily differences during the window [-90, 20], and then examine the evolution

of the cross-sectional mean of the cumulative difference (CDIF). Note that the results are

11 Recall that all four measures of general media coverage (MEDIA1, …, MEDIA4) are defined as the total of news per firm during the period 2000-2006.

18

highly similar if I take the absolute number of news stories including the word “downgrade”

only. However, I prefer to show the results based on the net measure CDIF because it is more

conservative in the sense that in controls for public information that might induce a market

reaction in the opposite direction. Furthermore, it is useful to differentiate the analysis by

event type (reviews for downgrade, actual downgrades) and general media coverage

(MEDIA1) because both factors are likely to influence the cumulative content of daily

corporate news. Figure 4a displays the evolution of CDIF (aggregated across rating agencies)

before negative rating events12 while Figure 4b shows the corresponding cumulative abnormal

CDS spread changes.


This analysis yields three interesting findings. First, it can be seen from Figure 4a that

there is a negative drift in the CDIF measure for firms with high general media coverage (left

axis) in pre event-time that intensifies the closer the event time to the announcement day.

Stated differently, the number of news including “downgrades”is more frequent than news

including “upgrade” when approaching the event time = 0. There is no clear pattern for firms

with low media coverage except a negative spike around the event data. In particular, there is

no signficant and systematic anticipation in pre event time. This result indicates that public

information for firms with high general media coverage is not only on average higher but

especially on individual days before rating events. Second, for firms with high general media

coverage it can be seen that the run up of public information starts clearly earlier for actual

downgrades (bold gray line) than for reviews for downgrade (bold black line), indicating that

rating reviews are less anticipated in the media. Third, when comparing Figures 4a and 4b, it

12 In addition, I have analyzed CDIF by event types and general media coverage for each rating agency separately. Since the observed pattern is highly similar across agencies, I only report on the aggregated results to conserve space.

19

turns out that the cumulative measure of public information that relates to rating changes (Fig.

4a) and the CDS market response (Fig. 4b) are strongly negatively correlated. This negative

link is strikingly strong for firms with high media coverage (Peason’s correlation coefficient

is -0.97 for downgrades and -0.93 for reviews for downgrade), suggesting that high media

coverage is associated with more efficient markets. In contrast, the correlation is substantially

weaker in the case of firms with low media coverage (-0.64 for downgrades and 0.24 for

reviews for downgrade).

I now turn to a formal multivariate regression analysis, linking the amount of public

information and the CDS market response before rating events. Since Figure 4 reveals that the

intensity of negative public information increases before negative rating events, I now include

indicator variables (PRENEGNEWS) 13 for days with negative rating-related wire news in the

interval [-11, -2] in the baseline regression model to study the impact of public information on

CDS spread changes. If theses indicator variables exhibit significantly positive coefficients (=

abnormal CDS spread changes on days with negative news) it is likely that CDS markets are

influenced by these news.14 Table 6 reports the regression results.


The analysis is conducted for rating events (reviews for downgrade, actual downgrades)

aggregated across all three agencies (Panel A) as well as by event types and rating agencies

separately (Panel B). The aggregate analysis confirms that there is a significantly positive

announcement effect for reviews for downgrade (RD). Most important, the indicator variable

13 These indicator variables cover a relatively short period of time to minimize potential contamination effects from other rating events. 14 Theoretically, there may be a reverse causality, i.e. the CDS market reaction may influence corporate news. However, given the practices of press agencies and media companies it seems unlikely that this effect dominates the effect from news to CDS markets. However, the recent research on “market implied ratings” started in 2006 may indeed increase two-way linkages between the public information and CDS prices (e.g., Munves, Jiang, and Lam, 2006).

20

PRENEGNEWS_RD also exhibits a significantly positive coefficient (3.27 basis points)

which is even slightly larger than the announcement effect (3.15 basis points). This result is

evidence in favor of the view that the short-term run up before rating reviews is related to

public information that is available to markets prior to the rating agencies’ announcements.

The differentiation by event types and rating agencies (Panel B) shows that negative news on

days before rating announcements are significantly positively related to CDS spread changes

for reviews for downgrade of S&P, Moody’s, and Fitch and downgrades of Moody’s and

Fitch (Models 1 and 2). Interestingly, CDS markets strongly react to negative news before

negative rating announcements made by Fitch, as can be seen from the significantly positive

coefficients of PRENEGNEWS_FRD in Model 1 (= 2.20) and PRENEGNEWS_FD in Model

2 (=2.41). This finding provides one explanation why related studies and previous analyses in

this paper have failed to find significant announcement effects for this rating agency. In other

words, Fitch seems to announce its rating actions not only relatively late in comparison to the

two other rating agencies (for firms with multiple ratings) but also late relative to already

available public information. Finally, the regression analysis including all events (Model 3)

reveals that only the news effect before reviews for downgrade of S&P and Moody’s remains

statistically significant.

Summarizing, the CDS market anticipation of rating events is indeed significantly related

to the intensity and content of the continuous stream of public information, suggesting that

these markets incorporate new public information relatively quickly.

6. Does private information influence the CDS market response to rating events?

6.1. The influence of the number of bank relationships

The above findings support the view that the CDS market response before negative rating

announcements is related to the intensity and content of public information about the traded

firms. I now examine if and how private information influences prices in CDS markets.

21

There is evidence that commercial and universal banks that lend to large firms can benefit

from exploiting private information about their borrowers through trading in CDS markets.

Hence, these banks may use “insider information” about future changes in the credit quality of

these firms (Acharya and Johnson, 2007a). Therefore, one can argue that the higher number of

major bank lenders of the firms traded in the CDS market the higher the probability of insider

trading because virtually all large banks also participate in the CDS market. Note that this

argument holds regardless whether there is much or little public information about the traded

firms. However, it may be the most plausible explanation for trading in CDS markets if there

is no public information (or exclusively unrelated public information).

To investigate this potential determinant of private-information based trading, I

subsequently split the sample of 95 firms in three groups including the same number of firms

conditional on their number of lead banks15, as reported in LPC Deal Scan (NUMBANK1 as

the lower tercile: 1-5, NUMBANK2 as the mid tercile: 6-7, and NUMBANK3 as the upper

tercile: 8-16). The hypothesis is that the run up before negative rating announcements starts

earlier and becomes stronger the higher the number of bank lenders (= potential CDS markets

participants with private information). Figure 5 displays the cumulative abnormal CDS spread

changes for the [-90, 20] interval around reviews for rating downgrades and actual rating

downgrades. I have aggregated the CDS market response across announcements of the three

major rating agencies to conserve space.16


15 As expected the number of major bank lenders (NUMBANK) is positively correlated with the firms’ average market capitalization (ρ=0.04) and their general media coverage (e.g., ρ(NUMBANK, MEDIA1)=0.13 and ρ(NUMBANK, MEDIA3)=0.26). However, given that this correlation is not very strong I expect that NUMBANK includes additional information that goes beyond firm size and public information. 16The results are highly similar for the CDS market response to each of the rating agencies separately.

22

This test yields several interesting findings. It turns out that the positive run up of the CDS

market starts earlier and is strongest for firms with a large number of major bank lenders

(upper tercile, solid black line). Interestingly, the magnitude of the run up of the upper tercile

is clearly most pronounced in absolute and relative terms. In addition, there is a monotonic

rank ordering of all three terciles on most of the days prior to the announcements (for reviews

for downgrade only during the interval [-40, -5]; for downgrades in the entire interval [-90,

0]). Moreover, the run up of the upper tercile before actual downgrades is particularly strong

in comparison to the mid and lower tercile. The latter can be explained by a reinforcing effect

of public and private information. In contrast, the reaction of the upper tercile is small in

relative terms for reviews for downgrades which are, on average, more surprising than actual

downgrades. These findings suggest that private information is likely to affect the CDS

market response prior to negative rating events since the amount of private information that is

leaked in CDS markets is expected to be higher for firms with a large number of bank

relationships.

6.2. The interaction of corporate news and abnormal CDS spread changes

Subsequently, I carry out a formal test to disentangle which part of the CDS market reaction is

due to public and which due to private information. As a start, I calculate daily abnormal CDS

spread changes (ASC) for the full sample on (i) all days, (ii) days with negative news, (iii)

days with no negative news, and (iv) days with no news. The full sample includes data from

all firms on all days, i.e. it also includes periods around rating announcements. These four

situations serve as a benchmark for the CDS market response prior to rating events. Recall

that ASCs are already corrected for the rating level which makes them comparable between

rating levels, i.e. they are calculated as raw CDS spread changes minus the change of the

rating-specific CDS index. It can be expected that CDS markets exhibit a significantly

positive reaction on days with negative news and an insignificant reaction on other days.

23

Then, I calculate the ASCs for similarly defined days during an uncontaminated 20-day

window17 before reviews for downgrades and actual downgrades by all three agencies. Given

the results from related studies and previous sections of this paper, it is reasonable to expect

significantly positive ASC for the average of all days in the 20-day window. The key question

now is what drives this result. In other words, which part can be attributed to days with

negative news, days with no negative news, and days with no news at all. Most important,

significantly positive ASC on days with no negative news or days with no news (=no public

information) would be consistent with the existence of CDS trading that is driven by private

information. Based on these results, I calculate the percentage of days with large positive

ASCs on days with no negative news and no news during the 20-day pre-event window and

compare it to the percentage in the full sample. Table 7 reports the results.


As expected, the upper part of Panel A reveals that there are significantly positive ASCs

on days with negative news in the full sample while I fail to find a significant abnormal CDS

market reaction on all days, days with no negative news, and days with no news. In addition,

the lower part of Panel A shows that there are significantly positive ASCs on average and on

days with negative news during the uncontaminated 20-day window before negative rating

announcements. Unsurprisingly, the magnitude of the pre-event window ASCs is roughly five

times bigger (1.74 basis points) than on days with negative news in the full sample (0.29 basis

points). Most important, I also detect significantly positive ASCs on days with no negative

news (0.25 basis points) and no news at all (0.30 basis points). This abnormal market reaction

cannot be explained by rating-related wire news which is the key source of public information

17 This window does not include any rating announcements from the three major rating agencies.

24

for CDS traders. Hence, the CDS market response on these days is consistent with the

existence of private-information based or insider trading.

Moreover, Panel B reports the percentage of firm-day observations with (i) large ASCs

and no negative news (INFO1) and (ii) large ASCs and no news (INFO2) in the full sample as

well as in the 20-day window before negative rating announcements. Large ASCs correspond

to ASCs that exceed the 90% quantile of the distribution of ASCs in the full sample (2.32

basis points per day). It can be seen that this fraction of suspicious days (INFO1) amounts to

15.82% before negative rating announcements, being almost twice as large as in the full

sample (8.97%). The same holds for the more conservative measure (INFO2) where I observe

9.99% suspicious days before negative rating events and only 5.78% in the full sample. The

differences in both measures are economically and statistically highly significant. Hence,

these results are further support for the view that private-information based CDS trading does

only exist on average but that it is also more frequent on days before negative rating

announcements.

In addition, I have carried out the same tests as in Table 7 (Panel A and B) for positive

rating announcements (reviews for upgrade, actual rating upgrades) by the three agencies. It

turns out that ASCs during an uncontaminated 20-day window before positive rating events

are not significantly different from zero, the ASCs are significantly positive on days with

positive news and, most important, insignificant on days with no positive news or no news.

Strikingly, the fraction of suspicious days in the 20-day pre-event window before positive

rating announcements is significantly lower than in the full sample.18 These additional

findings suggest that private information-based trading in CDS markets is asymmetric, i.e. is

is more likely to occur prior to credit quality deteriorations. This seems plausible because

18 INFO1 (no positive news and strong negative ASCs): 7.66% in the full sample, 5.63% in the 20-day window. INFO2 (no news and strong negative ASCs): 5.61% in the full sample, 3.30% in the 20-day window.

25

lenders can gain more from buying protection (either market value gains or full recovery in

the case of default) than from selling protection (only market value gains).

6.3. Determinants of the probability of private information-based trading

In this section, I complete the analysis by investigating which factors influence the

probability of suspicious CDS trading days, i.e. days on which the market reaction cannot be

explained with proxies for public information. To examine this issue, I estimate two

multivariate cross-sectional time series pooled probit regression models19 with INFO1

(INFO2) as dependent variables. Explanatory variables are the measure of general media

coverage (MEDIA1), a dummy variable indicating days from the [-20, -1]-window before

negative rating announcements (WINDOW), the firm’s credit rating assigned by Moody’s

(RATING, on a scale from 1 (Aaa) to 6 (B))20, dummy variables indicating financial

institutions (FIN), telecommunication companies (TEL), U.S. firms (US), the existence of at

least one split rating in a pair-wise comparison of the firm’s credit ratings (SPLIT), the mid-

and upper tercile of the number of bank lenders (NUMBANK2, NUMBANK3, with

NUMBANK1 as reference category), the relative CDS bid-ask spread as liquidity measure,

and year fixed effects. I expect negative coefficients for firms with a high general media

coverage (MEDIA1=1), for financial institutions (FIN=1), and for the liquidity measure

(BIDASK). Stated differently, private information-based CDS trading is less likely to occur if

the amount of firm-specific public information is high, in the case of banks as CDS

underlyings because informational advantages from lending to other banks are very unlikely,

and in times of low liquidity since the opportunity cost of being unable to exploit private

information is higher than in times of high liquidity.21 In contrast, positive coefficients are

19 Unsurprisingly, given the structure of the dataset the results are very similar for fixed effects panel models. 20 Taking the credit ratings from the other two agencies (S&P, Fitch) does not change the results. 21 If the liquidity in CDS markets is relatively low, i.e. the percentage bid ask spreads are high, the price impact of single transactions and the risk of no execution are higher than in periods of high liquidity.

26

expected for the time window before rating annoucements (WINDOW=1), for the variable

RATING because gains from CDS trading in riskier firms are higher than from trading in

low-risk firms, and for the number of bank relationships (NUMBANK2-3) since the higher

the number of major bank lenders the higher the number of potential insiders. Table 8 reports

the regression results.


The analysis indicates that suspicious firm-day observations (INFO1: large ASCs and no

negative news) are more likely during the 20-day window before negative rating events, the

worse the credit rating (here: the higher number the worse the rating), for non-financial firms

(= industrial companies), for U.S. firms, for firms with split ratings, for firms in the upper

tercile of the number of bank lenders (NUMBANK2), and the lower the relative bid-ask

spread of the CDS underlyings. These findings are consistent with all previous results, in

particular with the main message from Figure 5 on the number of bank lenders of CDS

underlyings. The regression analysis also offers some further insights. The general media

coverage does not affect the probability of private information-based CDS trading, confirming

that the dependent variable is reasonably defined. Apparently, private-information based CDS

trading is less likely to occur in CDS with financial institutions as reference entities. This is

consistent with the intuitively plausible view that banks can exploit private information from

lending to industrial firms but not from “wholesale” inter-bank lending. Moreover, I find that

insider trading is more likely to occur on relatively liquid days (relative low bid-ask spreads)

which appears to be reasonable because the opportunity costs arising from the risk of being

unable to exploit private information are lower on these days. Finally, when estimating the

probability of INFO2 (large ASCs and no news) I obtain very similar results with the

exception of the coefficients for MEDIA1 and US. The differently signed coefficient of

27

MEDIA1 is reasonable since it is a direct consequence of the change in the dependent

variable: firms with high general media coverage are on average less likely to exhibit days

with no news. The different sign for the dummy variable indicating U.S. firms shows that

these firms less often display days with no news.

Summarizing, the empirical evidence from this section suggests that there is private

information-based trading in CDS markets prior to rating events and in addition to trading

that can be explained by public information.

7. Further empirical checks

I conduct some additional empirical checks to study whether previous results are robust on

subsamples and how liquidity effects may influence the CDS market response to rating

announcements.

First, I have repeated the event study and regression analyses for subsamples including (i)

industrial firms vs. financial institutions, (ii) U.S. firms vs. European firms, and (iii) data from

the years 2000-2002 and 2003-2005. In all three checks I obtain results that confirm the key

findings from the full sample.

Second, the liquidity of CDS varies across firms and time (e.g., Longstaff, Mithal, Neis,

2005; Tang and Yan, 2007; Bühler and Trapp, 2008). Since the analysis in Section 4.4

indicates that private information-based CDS trading is more likely during the month prior to

rating announcements and when liquidity in the CDS market is high, I take a closer look at

the dynamics of CDS market liquidity prior to rating events. For this purpose, I compute the

absolute (difference between bid and ask CDS spread) and percentage bid-ask spread of CDS

(absolute bid-ask relative to mid, stated in %) and repeat the baseline event study. This

analysis yields that mean cumulative changes of absolute bid-ask spreads increase during the

[-90, 0] window and then remain relatively stable. Interestingly, the speed of this increase is

lower than the increase of cumulative abnormal mid CDS spread changes, leading to a steady

28

and almost monotonic decrease of the percentage bid-ask spread.22 In other words, CDS

markets become more liquid on days that are closer to the rating announcement because bid-

ask spreads widen at a lower speed than the mid CDS spread levels. To the best of my

knowledge this event-related liquidity effect has not been documented in other studies and is

consistent with the above finding that insider trading is more likely to occur when liquidity in

CDS markets is high.

8. Conclusions

This paper investigates if and how public and private information affects the CDS market

response to rating announcements. Analyzing an international sample of 95 frequently traded

firms during the period from 2000 to 2005 on a daily basis yields the following results.

First, I find significant announcement effects for reviews for downgrade and to a smaller

extent for downgrades. Moreover, there are also anticipations effects (run up) in CDS markets

that are stronger for actual downgrades than for rating reviews. Second, information that has

become public before rating agencies announce their actions is reflected in CDS markets. The

general media coverage of CDS underlyings relates to the timing and the magnitude of

anticipation and announcements effects. In addition, the intensity and content of daily

corporate news helps to explain the CDS market response. Third, I find also evidence that

private information influences CDS spreads before rating announcements under certain

conditions. Specifically, the anticipation of negative rating events becomes stronger the higher

the number of bank lenders of a CDS underlying, there is a clustering of days with no news

(or no negative news) and large significantly positive abnormal CDS spread changes, and

these days are likely to be observed when liquidity is relatively high and/or during the month

before rating announcements. Interestingly, the latter effects cannot be detected before 22 The mean cumulative changes of the percentage bid-ask spread of CDS, stated in percentage points, in the interval [-90, 0] prior to reviews for downgrade (downgrades) amounts to -3.8 (-1.0) for S&P, -4.5 (-3.3) for Moody’s, and -6.1 for Fitch (-4.8). Recall that the mean percentage bid-ask spread of CDS in the full sample is 22%.

29

positive rating announcements, suggesting that insider trading is more likely to occur before

credit quality deteriorations than before improvements.

This study has several important implications. With respect to the efficiency of CDS

markets, it has been shown that these markets do indeed adjust quickly and accurately to

public information. In addition, private information-based CDS trading drives the markets

into the right direction in particular prior to negative rating events, improving the overall price

formation process. This important effect should be considered in the recent debate about

potential regulation and supervision of CDS markets. Finally, with regard to the role of credit

rating agencies, it turns out that the surprisingness of rating announcements clearly differs

across agencies and event types. Rating reviews announced by S&P and Moody’s appear to

convey information to markets that has not been fully anticipated, underscoring the joint

importance of credit ratings and timely review actions by the rating agencies.

30

Appendix A. Sample composition

ABBEY NATIONAL PLC ABN AMRO BANK NV AKZO NOBEL NV ALLIED DOMECQ PLC AMERICAN EXPRESS CO AT&T CORP AVENTIS SA BAE SYSTEMS PLC BANCO BILBAO VIZCAYA ARGENTARIA SA BANCO SANTANDER CENTRAL HISPANO SA BANK OF AMERICA CORP BARCLAYS BANK PLC BASF AG BAYER AG BAYERISCHE HYPO-UND VEREINSBANK AG BEAR STEARNS COMPANIES INC BMW AG BNP PARIBAS SA BOEING CO BRITISH AIRWAYS PLC BRITISH AMERICAN TOBACCO PLC BT GROUP PLC (BRITISH TELECOM) CARREFOUR SA CATERPILLAR INC CITIGROUP INC COMMERZBANK AG COUNTRYWIDE HOME LOANS INC COX COMMUNICATIONS INC CREDIT LYONNAIS DAIMLERCHRYSLER AG DEERE AND CO DEUTSCHE BANK AG DEUTSCHE LUFTHANSA AG DEUTSCHE TELEKOM AG DIAGEO PLC DIXONS GROUP PLC DRESDNER BANK AG E.ON AG EASTMAN KODAK CO ENDESA (SPAIN) ENI SPA FIAT SPA FORD MOTOR CREDIT CO FRANCE TELECOM GENERAL MOTORS ACCEPTANCE CORP GOLDMAN SACHS GROUP INC, THE HILTON HOTELS CORP IBERDROLA SA

IMPERIAL CHEMICAL INDUSTRIES PLC ING BANK NV INTERNATIONAL BUSINESS MACHINES CORP INTERNATIONAL PAPER CO INTESABCI SPA JP MORGAN CHASE & CO KINGFISHER PLC KONINKLIJKE KPN NV KONINKLIJKE PHILIPS ELECTRONICS NV LAFARGE SA LEHMAN BROTHERS HOLDINGS INC LLOYDS TSB BANK PLC LOCKHEED MARTIN CORP MARKS & SPENCER MERRILL LYNCH CO INC METRO AG MORGAN STANLEY MOTOROLA INC NOKIA OYJ PEARSON PLC RAYTHEON CO RENAULT SA REPSOL YPF SA REUTERS GROUP PLC SAINSBURY J PLC SANPAOLO IMI SPA SEARS ROEBUCK ACCEPTANCE SIEMENS AG SOCIETE GENERALE STANDARD CHARTERED BANK SUEZ SA TARGET CORP TELEFONICA SA TESCO PLC TOTALFINAELF SA UBS AG UNICREDITO ITALIANO SPA UNILEVER PLC VATTENFALL AB VIACOM INC VODAFONE GROUP PLC VOLKSWAGEN AG VOLVO AB WACHOVIA CORP WAL-MART STORES INC WALT DISNEY CO, THE WELLS FARGO AND CO

31

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Table 1: Summary statistics

This table reports summary statistics on CDS spreads (in basis points) by credit ratings, the frequency of credit rating announcements by type and agency, the corporate news (general media coverage and daily corporate news), and the number of major bank lenders (lead banks) per firm (measured at the parent company level for firms and banks). Data refers to an international sample of 95 firms during the period 2000-2005.

Panel A: CDS spreads Mean Median Min Max N

CDS spread level (mid price, bps) 62 38 4 1250 148,850 CDS bid-ask spread (relative to mid, %) 22 21 0 166 137,538 CDS spread level S&P AAA-AA 25 20 4 243 29,383 CDS spread level S&P A 45 35 7 455 61,689 CDS spread level S&P BBB 113 72 16 1062 31,355 CDS spread level S&P BB-B 299 295 28 1138 2,997

Panel B: Credit rating announcements Agency, Event Reviews for

downgrade Actual

downgrades Reviews for

upgrade Actual

upgrades Total

S&P 99 125 11 41 276 Moody’s 119 116 30 42 307 Fitch 51 98 8 26 183 Total 269 339 49 109 766

Panel C: General media coverage and daily corporate news General media coverage (2000-2006)

Mean (Median)

Min Max

MEDIA1: All news per firm 47,667 (35,462)

5,629

223,686

MEDIA2: Wire news per firm 21,851 (15,659)

2,964

86,662

MEDIA3: All news (rating-related) per firm 6,662 (3,866)

578

39,351

MEDIA4: Wire news (rating-related) per firm 2,933 (1,874)

296

14,082

Firm-specific rating-related wire news (2000-2005)

Mean

Min

Max

News per day and firm 1.61 0 220 Words per day and firm 1,142.53 0 215,943 Positive news per day and firm 0.47 0 113 Negative news per day and firm 0.19 0 70 News per day and firm including “upgrade” 0.05 0 35 News per day and firm including “downgrade” 0.04 0 55

Panel D: Number of reported bank lenders of firms traded in CDS markets Mean Median Min Max

Number of reported bank lenders per firm, collected from LPC Deal Scan

6.36

6

1

16

35

Table 2: The CDS market response to rating announcements

Results are based on daily data for 95 firms during the period 2000-2005. The dependent variable is the daily raw CDS spread change (ΔCDSit). Explanatory variables are the change of the rating grade-specific CDS index (ΔIrt), dummy variables indicating the [-1, 1] interval around rating events (SRD = S&P announces a review for downgrade, …, FD = Fitch announces downgrade), dummy variables indicating the [-11, -2] interval before rating events (PRESRD = S&P announces a review for downgrade, …, PREFD = Fitch announces downgrade), dummy variables indicating firms from the telecommunications industry (TEL) and the U.S. (US), as well as year fixed effects. P-values in all regressions are based on robust standard errors considering the clustering on firms. ***, **, * denote coefficients that are statistically significant at the 0.01, 0.05, and 0.10-level.

(1) Announcement effects

(2) Announcement effects & short-term anticipation

Dep. Var.: ΔCDSit Coeff. p-val. Coeff. p-val. ΔIrt 0.5291 0.000 *** 0.5281 0.000 *** SRD 3.7790 0.005 *** 3.4469 0.009 *** MRD 2.6890 0.019 ** 2.3538 0.041 ** FRD -0.2214 0.854 -0.4789 0.694 SD 0.6816 0.395 1.1374 0.147 MD 2.3833 0.003 *** 2.6818 0.001 *** FD 0.3269 0.706 0.0166 0.985 PRESRD --- 1.2969 0.001 *** PREMRD --- 1.3204 0.000 *** PREFRD --- 0.5687 0.246 PRESD --- -1.3457 0.002 ** PREMD --- -1.0467 0.012 ** PREFD --- 0.2197 0.697 TEL -0.0325 0.046 ** -0.0579 0.014 ** US 0.0031 0.834 -0.0028 0.841 YEAR dummies Yes Yes Constant 0.0343 0.003 *** 0.0183 0.084 * N 148,580 148,580 Adj. R2 0.0470 0.0480

36

Table 3: Run up of CDS markets before rating announcements

Run up (in %) is calculated as the mean cumulative abnormal CDS spread changes at event time t divided by the mean cumulative abnormal CDS spread change at event time 0 (end of the announcement day). The sample period is 2000-2005 and includes 269 reviews for downgrade and 339 actual downgrades by S&P, Moody’s, and Fitch.

Reviews for rating downgrade Actual rating downgrades Event time S&P Moody’s Fitch S&P Moody’s Fitch Cumulative [-90, -11] 45.61 56.00 61.33 84.84 73.55 86.70 run up [-10, -1] 42.85 33.54 24.22 11.87 17.08 11.20 Average [-90, -11] 0.24 0.34 0.34 0.45 0.40 0.54 run up [-10, -1] 4.56 5.60 6.13 8.48 7.35 8.67 per day 0 11.54 10.46 14.45 3.29 9.37 2.10

37

Table 4: The CDS market response to rating announcements by general media coverage

Results are based on daily data for 95 firms during the period 2000-2005. MEDIA4 differentiates between firms that exhibit a total of rating-related wire news that is below (Low media coverage) or above (High media coverage) the sample median. The dependent variable is the daily raw CDS spread change (ΔCDSit). Explanatory variables are the change of the rating grade-specific CDS index (ΔIrt), dummy variables indicating the [-1, 1] interval around rating events (SRD = S&P announces a review for downgrade, …, FD = Fitch announces downgrade), dummy variables indicating the [-11, -2] interval before rating events (PRESRD = S&P announces a review for downgrade, …, PREFD = Fitch announces downgrade), dummy variables indicating firms from the telecommunications industry (TEL) and the U.S. (US), as well as year fixed effects. P-values in all regressions are based on robust standard errors considering the clustering on firms. ***, **, * denote coefficients that are statistically significant at the 0.01, 0.05, and 0.10-level.

Panel A: Announcement effects

Low media coverage (MEDIA4=0)

High media coverage (MEDIA4=1)

Dep. Var.: ΔCDSit Coeff. p-val. Coeff. p-val. ΔIrt 0.3813 0.000 *** 0.8718 0.000 *** SRD 3.7190 0.126 3.5137 0.004 *** MRD 1.8998 0.247 3.4209 0.036 ** FRD 0.9047 0.726 -1.0424 0.404 SD 0.8223 0.342 0.4824 0.714 MD 1.2415 0.148 3.1735 0.006 *** FD -1.5765 0.251 1.2620 0.214 TEL -0.0398 0.104 -0.0340 0.075 * US -0.0183 0.028 ** 0.0043 0.856 YEAR dummies Yes Yes Constant 0.0221 0.061 * 0.0387 0.054 * N 75,072 73,508 R2 0.0300 0.0873

Panel B: Announcement effects and short-term anticipation Low media coverage

(MEDIA4=0) High media coverage

(MEDIA4=1) Dep. Var.: ΔCDSit Coeff. p-val. Coeff. p-val. ΔIrt 0.3805 0.000 *** 0.8703 0.000 *** PRESRD 1.2658 0.033 ** 1.2241 0.014 ** SRD 3.3318 0.158 3.2465 0.009 *** PREMRD 1.2964 0.010 *** 1.3778 0.007 *** MRD 1.5467 0.336 3.1260 0.060 ** PREFRD 1.3127 0.165 0.1926 0.717 FRD 0.8729 0.729 -1.3717 0.298 PRESD -0.8677 0.125 -1.6454 0.008 *** SD 1.0516 0.132 1.0890 0.421 PREMD -0.9610 0.009 *** -1.2396 0.100 * MD 1.5176 0.081 * 3.5223 0.005 *** PREFD -1.3471 0.138 0.9101 0.112 FD -1.2015 0.357 0.6901 0.500 TEL -0.0519 0.047 ** -0.0604 0.043 ** US -0.0208 0.097 -0.0045 0.837 YEAR dummies Yes Yes Constant -0.0005 0.973 0.0309 0.072 * N 75,072 73,508 R2 0.0310 0.0887

38

Table 5: Run up of CDS markets before rating announcements by general media coverage

Run up (in %) is calculated as the mean cumulative abnormal CDS spread changes at event time t divided by the mean cumulative abnormal CDS spread change at event time 0 (end of the announcement day). The sample period is 2000-2005 and includes 269 reviews for downgrade and 339 actual downgrades by S&P, Moody’s, and Fitch. MEDIA4 differentiates between firms that exhibit a total of rating-related wire news that is below (MEDIA4=0, “Low”) or above (MEDIA4=1, “High”) the sample median.

Panel A: Reviews for rating downgrade S&P Moody’s Fitch Event time Low

coverage High

coverage Low

coverage High

coverage Low

coverage High

coverage Cumulative [-90, -11] 25.71 57.17 54.38 57.66 38.25 75.04 run up [-10, -1] 62.30 31.64 33.58 33.40 32.53 25.14 Average [-90, -11] 0.32 0.71 0.68 0.72 0.48 0.94 run up [-10, -1] 6.23 3.16 3.36 3.34 3.25 2.51 per day 0 11.99 11.19 12.04 8.95 29.22 -0.17

Panel B: Actual rating downgrades S&P Moody’s Fitch Event time Low

coverage High

coverage Low

coverage High

coverage Low

coverage High

coverage Cumulative [-90, -11] 73.73 89.14 71.65 75.01 89.59 85.81 run up [-10, -1] 22.90 7.55 16.46 14.66 11.13 11.11 Average [-90, -11] 0.92 1.11 0.90 0.94 1.12 1.07 run up [-10, -1] 2.29 0.75 1.65 1.47 1.11 1.11 per day 0 3.36 3.32 11.89 10.34 -0.72 3.08

39

Table 6: The influence of negative corporate news on the CDS market response before rating announcements Results are based on daily data for 95 firms during the period 2000-2005. The dependent variable is the daily raw CDS spread change (ΔCDSit). Explanatory variables are the change of the rating grade-specific CDS index (ΔIrt), dummy variables indicating the [-1, 1] interval around rating events (RD, D, SRD, SD, etc.), dummy variables indicating negative wire news during the [-11, -2] interval before rating events (PRENEGNEWS_...), dummy variables indicating firms from the telecommunications industry (TEL) and the U.S. (US), as well as year fixed effects. P-values in all regressions are based on robust standard errors considering the clustering on firms. ***, **, * denote coefficients that are statistically significant at the 0.01, 0.05, and 0.10-level.

Panel A: Results for aggregate rating events Dep. Var.: ΔCDSit Coeff. p-val. ΔIrt 0.5282 0.000 *** PRENEGNEWS_RD 3.2785 0.002 *** RD 3.1546 0.000 *** PRENEGNEWS_D -1.1096 0.315 D 0.7464 0.162 TEL -0.0589 0.002 *** US -0.0046 0.679 YEAR dummies Yes Constant 0.0314 0.002 *** N 148,580 Adj. R2 0.0470

Panel B: Results differentiated by event type and agencies

Model 1: Reviews for rating downgrade only

Model 2: Actual rating downgrades only

Model 3: All events

Dep. Var.: ΔCDSit Coeff. p-val. Coeff. p-val. Coeff. p-val. ΔIrt 0.5279 0.000 *** 0.5278 0.000 *** 0.5281 0.000 *** PRENEGNEWS_SRD 1.7995 0.012 ** 2.8396 0.063 ** SRD 3.8227 0.004 *** 3.6998 0.007 *** PRENEGNEWS_MRD 2.3797 0.000 *** 2.5186 0.010 *** MRD 3.3693 0.005 *** 2.4594 0.035 ** PRENEGNEWS_FRD 2.1983 0.003 *** 1.6811 0.217 FRD -0.3571 0.741 -0.4304 0.721 PRENEGNEWS_SD 0.8396 0.232 -2.0282 0.220 SD 1.9611 0.023 ** 0.7074 0.365 PRENEGNEWS_MD 1.6731 0.062 * -0.4204 0.757 MD 3.2202 0.000 *** 2.3197 0.003 *** PRENEGNEWS_FD 2.4145 0.000 *** 0.6743 0.627 FD -0.0811 0.928 0.0004 0.998 TEL -0.0588 0.005 *** -0.0398 0.018 ** -0.0616 0.005 *** US -0.0047 0.676 0.0001 0.992 -0.0051 0.651 YEAR dummies Yes Yes Yes Constant 0.0343 0.001 *** 0.0377 0.000 *** 0.0321 0.001 *** N 148,580 148,580 148,580 Adj. R2 0.0480 0.0460 0.0480

40

Table 7: Abnormal CDS spread changes conditional on public information

Panel A reports the magnitude of daily abnormal CDS spread changes (ASC) for the full sample and for an uncontaminated 20-day window before reviews for downgrade and downgrades. Panel B reports the relative frequency of suspicious firm-day observations, i.e. either days with no negative rating-related wire news and very large abnormal CDS spread changes (INFO1) or days with no rating-related wire news and very large abnormal CDS spread changes (INFO2). The latter are defined as abnormal CDS spread changes that exceed the 90%-percentile in the full sample (2.32 basis points per day). Results are based on data from 95 firms during the period 2000-2005.

Panel A: Magnitude of abnormal CDS spread changes (1) (2) (3) (4) Full sample All

days p-val.

(t-test) Days with

negative news p-val.

(t-test) Days with

no negative news p-val.

(t-test) Days with

no news p-val.

(t-test)

Mean 0.0252 0.1248 0.2941 0.0000 *** -0.0028 0.8644 0.0017 0.9419 Median 0.0000 0.0000 0.0000 0.0000 P90 2.3261 2.8359 2.2814 2.2496 P95 4.4620 6.0710 4.3240 4.2470 N (firm-day obs.) 148,580 14,072 134,508 84,795 Uncontaminated 20-day window before reviews for downgrade and downgrades

Mean 0.4158 0.0003 *** 1.7366 0.0008 *** 0.2510 0.0273 ** 0.2966 0.0497 ** Median 0.0000 0.1136 0.0000 0.0000 P90 4.9475 11.5644 4.6172 4.0863 P95 10.4742 20.9250 9.0640 7.6155 N (firm-day obs.) 7,310 811 6,499 4,145

Panel B: Relative frequency of suspicious firm-day observations

(1) INFO1 Percentage of suspicious firm-day observations

(no negative news, ASC > P90(ASC) in full sample)

(2) INFO2 Percentage of suspicious firm-day observations

(no news, ASC > P90(ASC) in full sample) Full sample

8.97

5.78

Uncontaminated 20-day window before reviews for downgrade and downgrades

15.82

9.99

P-val. (Binomial test) 0.0000 0.0000 *** ***

41

Table 8: The probability of CDS trading based on private information

This table reports results from probit regression models to estimate the probability of INFO1=1 (days with no negative news and large abnormal CDS spread changes) and INFO2=1 (days with no news and large abnormal CDS spread changes). Large abnormal CDS spread changes are defined as abnormal CDS spread changes that exceed the 90%-percentile in the full sample (2.32 basis points per day). Explanatory variables are the measure of general media coverage based on all news (MEDIA1), dummy variables indicating the [-20, -1] window before reviews for downgrade and actual downgrades by any of the three rating agencies, the credit rating assigned by Moody’s, dummy variables indicating financial institutions (FIN), telecommunication companies (TEL), U.S. firms (US), firms with split ratings (at least two agencies have assigned different ratings), the number of major bank lenders (NUMBANK2 for the mid tercile and NUMBANK3 for the upper tercile; the lower terciles serves as reference category), the bid-ask spread of the CDS (relative to the mid CDS spread level), and year dummies. Results are based on data from 95 firms during the period 2000-2005. P-values in all regressions are based on standard errors considering the clustering on firms. ***, **, * denote coefficients that are statistically significant at the 0.01, 0.05, and 0.10-level.

(1) (2) Dep.Var.: Prob(INFO1=1) Prob(INFO2=1) Coeff. p-val. Coeff. p-val. MEDIA1 -0.0049 0.897 -0.2234 0.000 *** WINDOW[-20, -1] 0.1665 0.000 *** 0.1403 0.001 *** RATING Moody’s 0.3013 0.000 *** 0.3724 0.000 *** FIN -0.0708 0.092 * -0.0879 0.129 TEL 0.0093 0.862 -0.0706 0.506 US 0.1008 0.006 *** -0.1168 0.040 ** SPLIT 0.0744 0.011 ** 0.0393 0.330 NUMBANK2 0.0419 0.299 0.0357 0.539 NUMBANK3 0.1101 0.014 ** 0.1031 0.091 * BIDASK -1.1696 0.000 *** -0.6286 0.000 *** YEAR dummies Yes Yes Const. -2.0565 0.000 *** -2.4221 0.000 *** N 123,230 118,678 Pseudo-R2 0.1102 0.1192

42

Figure 1: Mean cumulative abnormal CDS spread changes by event type

Mean cumulative abnormal CDS spreads changes (CASC) are calculated as the cumulative sum of the daily cross-sectional mean abnormal CDS spread changes at event time t, starting 90 days prior to a rating announcement. The sample period is 2000-2005 and includes 269 reviews for downgrade and 339 actual downgrades by S&P (solid black line), Moody’s (broken black line), and Fitch (gray line).

Fig. 1a: Reviews for rating downgrade

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Fitch

Fig. 1b: Actual rating downgrades

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43

Figure 2: Mean cumulative CDS spread changes by event type, rating agency and general media coverage

Mean cumulative abnormal CDS spreads changes (CASC) are calculated as the cumulative sum of the daily cross-sectional mean abnormal CDS spread changes at event time t, starting 90 days prior to a rating announcement. The sample period is 2000-2005 and includes 269 reviews for downgrade and 339 actual downgrades by S&P, Moody’s, and Fitch. The black (gray) line displays the CASC of firms with relatively high (low) general media coverage. General media coverage (MEDIA1) is defined by means of a median split based on the total of all news per firm in FACTIVA. Fig. 2a: S&P, review for downgrade Fig. 2b: Moody’s, review for downgrade Fig. 2c: Fitch, review for downgrade

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Figure 3: Intensity and content of daily corporate news by event type This figure depicts the mean number of all wire news per day (thin black line, left axis), negative wire news (bold black line, right axis) and wire news including “downgrade” (bold gray line, right axis) prior to reviews for downgrade and downgrades by rating agencies. Numbers correspond to the average across rating agencies.


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45

Figure 4: The content of corporate news by event type and general media coverage

Figure 4a depicts the mean cumulative difference between wire news including “upgrade” and “downgrade” (CDIF) prior to reviews for downgrade and downgrades by rating agencies. Bold (broken) lines indicate CDIF for firms with high (low) general media coverage on the left (right) axis. Black (gray) lines refer to reviews for rating downgrade (actual rating downgrades). General media coverage (MEDIA1) is defined by means of a median split based on the total of all news per firm in FACTIVA. Numbers are averaged across rating agencies. Figure 4b displays mean cumulative abnormal CDS spread changes for the corresponding cases.

Fig. 4a: Cumulative difference between news including “upgrade” and “downgrade”

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igh)

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Fig. 4b: Cumulative abnormal CDS spread changes

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Reviews for downgrade, lowReviews for downgrade, highDowngrades, lowDowngrades, high

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Figure 5: CDS market response by number of major bank lenders of the CDS underlyings

Mean cumulative abnormal CDS spreads changes (CASC) are calculated as the cumulative sum of the daily cross-sectional mean abnormal CDS spread changes at event time t, starting 90 days prior to a rating announcement. CDS underlyings are classified into terciles based on the number of their reported lead banks in LPC Deal Scan (lower tercile: 1-5, mid tercile: 6-7, upper tercile: 8-16). The sample period is 2000-2005 and includes 269 reviews for downgrade and 339 actual downgrades that are aggregated across rating agencies.


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